Reference is made to FIG. 1, which is a simplified diagram of a prior art enterprise network 100 connected to an external internet 10. Network 100 is shown generally with resources including computers 110, servers 120, switches and routers 130, and mobile devices 140 such as smart phones and tablets, for ease of presentation, although it will be appreciated by those skilled in the art that enterprise networks today are generally much more varied and complex and include other devices such as printers, phones and any Internet of Things objects. The various connections shown in FIG. 1 may be direct or indirect, wired or wireless communications, or a combination of wired and wireless connections. Computers 110 and servers 120 may be physical elements or logical elements, or a mix of physical and logical elements. Computers 110 and servers 120 may be physical or virtual machines. Computers 110 and servers 120 may be local, remote or cloud-based elements, or a mix of local, remote and cloud-based elements. Computers 110 may be client workstation computers. Servers 120 may be file transfer protocol (FTP) servers, email servers, structured query language (SQL) servers, secure shell (SSH) servers, and other database and application servers.
Access to computers 110 and servers 120 in network 100 may optionally be governed by an access governor 150, such as a directory service, that authorizes users to access computers 110 and servers 120 based on “credentials” and other methods of authentication. Access governor 150 may be a name directory, such as ACTIVE DIRECTORY® developed by Microsoft Corporation of Redmond, Wash., for WINDOWS® environments. Background information about ACTIVE DIRECTORY® is available at Wikipedia. Other access governors for WINDOWS and non-WINDOWS environments include inter alia Lightweight Directory Access Protocol (LDAP), Remote Authentication Dial-In User Service (RADIUS), and Apple Filing Protocol (AFP), formerly APPLETALK®, developed by Apple Inc. of Cupertino, Calif. Background information about LDAP, RADIUS and AFP is available at Wikipedia.
Access governor 150 may be one or more local machine access controllers. For networks that do not include an access governor, authentication may be performed by other servers 120. Alternatively, in lieu of access governor 150, resources of network 100 determine their local access rights.
Credentials for accessing computers 110 and servers 120 include inter alia server account credentials such as <address> <username> <password> for an FTP server, a database server, or an SSH server. Credentials for accessing computers 110 and servers 120 also include user login credentials <username> <password>, or <username> <ticket>, where “ticket” is an authentication ticket, such as a ticket for the Kerberos authentication protocol or NTLM hash used by Microsoft Corp., or login credentials via certificates or via another implementation used today or in the future. Background information about the Kerberos protocol and LM hashes is available at Wikipedia.
Access governor 150 may maintain a directory of computers 110, servers 120 and their users. Access governor 150 authorizes users and computers, assigns and enforces security policies, and installs and updates software.
Computers 110 may run a local or remote security service, which is an operating system process that verifies users logging in to computers, to single sign-on systems, and to credential storage systems.
Network 100 may include a security information and event management (SIEM) server 160, which provides real-time analysis of security alerts generated by network hardware and applications. Background information about SIEM is available at Wikipedia.
Network 100 may include a domain name system (DNS) server 170, or such other name service system, for translating domain names to IP addresses. Background information about DNS is available at Wikipedia.
Network 100 may include a firewall 180 located within a gateway between enterprise network 100 and external internet 10. Firewall 180 controls incoming and outgoing traffic for network 100. Background information about firewalls is available at Wikipedia.
One of the most prominent threats that organizations face is a targeted attack; i.e., an individual or group of individuals that attacks the organization for a specific purpose, such as stealing data, using data and systems, modifying data and systems, and sabotaging data and systems. Targeted attacks are carried out in multiple stages, typically including inter alia reconnaissance, penetration and lateral movement. Lateral movement involves orientation, movement and propagation, and includes establishing a foothold within the organization and expanding that foothold to additional systems within the organization.
In order to carry out the lateral movement stage, an attacker, whether a human being who is operating tools within the organization's network, or a tool with “learning” capabilities, learns information about the environment it is operating in, such as network topology, network devices and organization structure, learns “where can I go from my current location” and “how can I move from my current location to another location (privilege required)”, learns implemented security solutions, learns applications that he can leverage, and then operates in accordance with that data.
An advanced attacker may use different attack techniques to enter a corporate network and to move laterally within the network in order to obtain his resource goals. The advanced attacker may begin with a workstation, server or any other network entity to start his lateral movement. He uses different methods to enter the network, including inter alia social engineering, existing exploits and vulnerabilities, and a Trojan horse or any other malware allowing him to control a first node or nodes.
Once an attacker has taken control of a first node in a corporate network, he uses different advanced attack techniques for orientation and propagation and discovery of additional ways to reach other network nodes in the corporate network. Attacker movement from node to node is performed via an “attack vector”, which is an object discovered by the attacker, including inter alia an object in memory or storage of a first computer that may be used to access or discover a second computer.
Exemplary attack vectors include inter alia credentials of users with escalated privileges, existing shared location names stored on different servers and workstations, and details including the address and credentials of an FTP server, an email server, a database server or an SSH server. Attack vectors are often available to an attacker because a user did not log off from a workstation, did not log out of an application, or did not clear his cache. E.g., if a user contacted a help desk and gave a help desk administrator remote access to his workstation and if the help desk administrator did not properly log off from the remote access session to the user's workstation, then the help desk access credentials may still be stored in the user's local cache and available to the attacker. Similarly, if the user accessed a server, e.g., an FTP server, then the FTP account login parameters may be stored in the user's local cache or profile and available to the attacker.
Attack vectors enable inter alia a move from workstation A→server B based on a shared server host name and its credentials, connection to a different workstation using local admin credentials that reside on a current workstation, and connection to an FTP server using specific access credentials.
Whereas IT “sees” the logical and physical network topology, an attacker that lands on the first network node or nodes “sees” attack vectors that depart from that node and move laterally to other nodes. The attacker can move to such nodes and then follow “attack paths” by successively discovering attack vectors from node to node.
When the attacker implements such a discovery process on all nodes in the network, he will be able to “see” all attack vectors of the corporate network and generate a “complete attack map”. Before the attacker discovers all attack vectors on network nodes and completes the discovery process, he generates a “current attack map” that is currently available to him.
An objective of the attacker is to discover an attack path that leads him to a target network node. The target may be a bank's authorized server that is used by the corporation for ordering bank account transfers of money, it may be an FTP server that updates the image of all corporate points of sale, it may be a server or workstation that stores confidential information such as source code and secret formulas of the corporation, or it may be any other network nodes that are of value to the attacker and are his “attack goal nodes”.
When the attacker lands on the first node, but does not know how to reach the attack goal node, he generates a current attack map that leads to the attack goal node.
One method to defend against such attacks, termed “honeypots”, is to plant and monitor bait resources, with the objective that the attacker learn of their existence and then consume those bait resources, and to notify an administrator of the malicious activity. Background information about honeypots is available at Wikipedia.
Conventional honeypot systems operate by monitoring access to a supervised element in a computer network, the supervised element being a fake server or a fake service. Access monitoring generates many false alerts, caused by non-malicious access from automatic monitoring systems and by user mistakes. Conventional systems try to mitigate this problem by adding a level of interactivity to the honeypot, and by performing behavioral analysis of suspected malware if it has infected the honeypot itself.
Deception systems are used by organizations in order to deceive attackers into making detectable actions. However, attackers attempt to detect and avoid deceptions. When persistent attackers fail to progress, they try again and again until they find a successful path. They do so by elements within the environment.
Conventional deception systems like honeypots are flawed by being static, which allows the attacker to learn of their deceptions in ways such as the following.                Found deceptions—if an attacker previously acted on deceptive data and was caught, he may know not to stumble upon that same deception again.        Static deceptions—enterprise environments change over time. Static deceptions that do not change with the enterprise environment stand out as being different and, as such, may indicate a deception fingerprint.        Stale deceptions—if an attacker finds a deception element that has not been active for a long time, the attacker identifies it as being deceptive and avoids it.        Unfit deceptions—if an attacker finds a deception element that does not fit the enterprise environment, or that does not conform to an enterprise convention, it may stand out as being different and, as such, may indicate a deception fingerprint.        Uniform deceptions—if an attacker finds a deception element that exists on all or most computers, it may stand out and as such, may indicate a deception fingerprint.        
When creating and using deceptive environments used for deceiving attackers, it is important that the deceptive environment naturally fit in the enterprise network environment and change along with it. In this changing enterprise environment, static non-diversified and unchanging deceptive environments are not effective in deceiving, and hence deceptive environments need to become dynamic and to adapt to changes that occur in the enterprise environment.